Ses Kullanarak Otomatik Yerel İkili Model Yöntemi ile Gemi Tanımlama

Year 2020, Volume: 4 Issue: 1, 57 - 63, 10.08.2020

Türker Tuncer , Emrah Aydemir

Abstract

Ses sınıflaması, makine öğrenimi ve uygulamalı bilgisayar bilimleri için en önemli araştırma konularından biridir. Ses sınıflandırma yöntemi kullanılarak literatürde birçok biyometrik uygulama / yöntem sunulmuştur. Bu çalışma sesleri kullanarak bir gemi tanımlama yöntemi sunmaktadır. Sunulan bu yöntem çok basit ve etkilidir. Bu yöntemin sadece iki temel fazı vardır ve bu fazlar, bir boyutlu ikili örüntü (1D-BP) ile özellik çıkarma ve geleneksel sınıflandırıcı fazlarla sınıflandırmadır. 1D-BP her sesten 256 özellik çıkarır ve bu sesler sınıflandırıcılara iletilir. Bu ultra hafif ses tanımlama yöntemini test etmek için YouTube'dan bir gemi sesleri veri kümesi toplandı. Sonuçlara göre, bu yöntem %97 sınıflandırma doğruluğu elde etmiştir. Bu sonuçlar, gemi ses sınıflandırması ve ses tabanlı gemi tanımlaması üzerine sunulan 1D-BP tabanlı yöntemin değerini açıkça göstermiştir.

Keywords

Ses kullanarak gemi tanımlama, 1D-BP, Sınıflandırma, Ses işleme, Makine Öğrenmesi

An Automated Local Binary Pattern Ship Identification Method by Using Sound

Abstract

Sound classification one of the most important research issues for machine learning and applied computer sciences. By using sound classification method, many biometric applications/methods have been presented in the literature. This work presents a ship identification method by using sounds. This presented method is very simple and effective. This method has only two fundamental phases and these phases are feature extraction by one dimensional binary pattern (1D-BP) and classification with conventional classifiers phases. 1D-BP extracts 256 features from each sound and these sounds are forwarded to classifiers. To test this ultra-lightweight sound identification method, a ship sounds dataset was collected from YouTube. According to results, this method achieved 97% classification accuracy. This results clearly demonstrated merit of the presented 1D-BP based method on ship sound classification and sound based ship identification.

Keywords

Ship identification by using sound, 1D-BP, Classification, Sound processing, Machine Learning

References

• Badem, H. (2019). Parkinson Hastaliğinin Ses Sinyalleri Üzerinden Makine Öğrenmesi Teknikleri ile Tanimlanmasi. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 8(2), 630-637.
• Çalik, N., Ata, L. D., Serbes, A., Bolat, B., & Yavuz, E. (2015, May). Performance analysis of feature extraction methods in indoor sound classification. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 2025-2028). IEEE.
• George, J., Cyril, A., Koshy, B. I., & Mary, L. (2013). Exploring sound signature for vehicle detection and classification using ANN. International Journal on Soft Computing, 4(2), 29.
• Mesaros, A., Heittola, T., & Virtanen, T. (2016, August). TUT database for acoustic scene classification and sound event detection. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 1128-1132). IEEE.
• Khan, S., Divakaran, A., & Sawhney, H. S. (2009, May). Weapon identification using hierarchical classification of acoustic signatures. In Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII (Vol. 7305, p. 730510). International Society for Optics and Photonics.
• Liang, H., & Nartimo, I. (1998, October). A feature extraction algorithm based on wavelet packet decomposition for heart sound signals. In Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No. 98TH8380) (pp. 93-96). IEEE.
• Wang, J. C., Wang, J. F., He, K. W., & Hsu, C. S. (2006, July). Environmental sound classification using hybrid SVM/KNN classifier and MPEG-7 audio low-level descriptor. In The 2006 IEEE international joint conference on neural network proceedings (pp. 1731-1735). IEEE.
• Chen, C. H., Huang, W. T., Tan, T. H., Chang, C. C., & Chang, Y. J. (2015). Using k-nearest neighbor classification to diagnose abnormal lung sounds. Sensors, 15(6), 13132-13158.
• Gupta, C. N., Palaniappan, R., Swaminathan, S., & Krishnan, S. M. (2007). Neural network classification of homomorphic segmented heart sounds. Applied soft computing, 7(1), 286-297.
• Xia, X., Togneri, R., Sohel, F., & Huang, D. (2017, July). Random forest classification based acoustic event detection. In 2017 IEEE International Conference on Multimedia and Expo (ICME) (pp. 163-168). IEEE.
• Romero, J., Luque, A., & Carrasco, A. (2017, February). Animal Sound Classification using Sequential Classifiers. In BIOSIGNALS (pp. 242-247).
• Williams, R., Wright, A. J., Ashe, E., Blight, L. K., Bruintjes, R., Canessa, R., ... & Hammond, P. S. (2015). Impacts of anthropogenic noise on marine life: Publication patterns, new discoveries, and future directions in research and management. Ocean & Coastal Management, 115, 17-24.
• Farcas, A., Thompson, P. M., & Merchant, N. D. (2016). Underwater noise modelling for environmental impact assessment. Environmental Impact Assessment Review, 57, 114-122.
• Xi-ying, H., Jin-fang, C., Guang-jin, H., & Nan, L. (2010, May). Application of BP neural network and higher order spectrum for ship-radiated noise classification. In 2010 2nd International Conference on Future Computer and Communication (Vol. 1, pp. V1-712). IEEE.
• Chen, J., Li, H., Tang, S., & Sun, J. (2002, June). A SOM-based probabilistic neural network for classification of ship noises. In IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions (Vol. 2, pp. 1209-1212). IEEE.
• Yang, S., Li, Z., & Wang, X. (2002). Ship recognition via its radiated sound: The fractal based approaches. The Journal of the Acoustical Society of America, 112(1), 172-177.